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    {
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        "%matplotlib inline"
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      "source": [
        "\n# Gradient Boosting regression\n\n\nDemonstrate Gradient Boosting on the Boston housing dataset.\n\nThis example fits a Gradient Boosting model with least squares loss and\n500 regression trees of depth 4.\n\n"
      ]
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      "source": [
        "print(__doc__)\n\n# Author: Peter Prettenhofer <peter.prettenhofer@gmail.com>\n#\n# License: BSD 3 clause\n\nimport numpy as np\nimport matplotlib.pyplot as plt\n\nfrom sklearn import ensemble\nfrom sklearn import datasets\nfrom sklearn.utils import shuffle\nfrom sklearn.metrics import mean_squared_error\n\n# #############################################################################\n# Load data\nboston = datasets.load_boston()\nX, y = shuffle(boston.data, boston.target, random_state=13)\nX = X.astype(np.float32)\noffset = int(X.shape[0] * 0.9)\nX_train, y_train = X[:offset], y[:offset]\nX_test, y_test = X[offset:], y[offset:]\n\n# #############################################################################\n# Fit regression model\nparams = {'n_estimators': 500, 'max_depth': 4, 'min_samples_split': 2,\n          'learning_rate': 0.01, 'loss': 'ls'}\nclf = ensemble.GradientBoostingRegressor(**params)\n\nclf.fit(X_train, y_train)\nmse = mean_squared_error(y_test, clf.predict(X_test))\nprint(\"MSE: %.4f\" % mse)\n\n# #############################################################################\n# Plot training deviance\n\n# compute test set deviance\ntest_score = np.zeros((params['n_estimators'],), dtype=np.float64)\n\nfor i, y_pred in enumerate(clf.staged_predict(X_test)):\n    test_score[i] = clf.loss_(y_test, y_pred)\n\nplt.figure(figsize=(12, 6))\nplt.subplot(1, 2, 1)\nplt.title('Deviance')\nplt.plot(np.arange(params['n_estimators']) + 1, clf.train_score_, 'b-',\n         label='Training Set Deviance')\nplt.plot(np.arange(params['n_estimators']) + 1, test_score, 'r-',\n         label='Test Set Deviance')\nplt.legend(loc='upper right')\nplt.xlabel('Boosting Iterations')\nplt.ylabel('Deviance')\n\n# #############################################################################\n# Plot feature importance\nfeature_importance = clf.feature_importances_\n# make importances relative to max importance\nfeature_importance = 100.0 * (feature_importance / feature_importance.max())\nsorted_idx = np.argsort(feature_importance)\npos = np.arange(sorted_idx.shape[0]) + .5\nplt.subplot(1, 2, 2)\nplt.barh(pos, feature_importance[sorted_idx], align='center')\nplt.yticks(pos, boston.feature_names[sorted_idx])\nplt.xlabel('Relative Importance')\nplt.title('Variable Importance')\nplt.show()"
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